032 Reducing high-risk psychiatric medication in youth: a solution-focused discussion
Notice bibliographique
Résumé
In this seminar, we aim to engage the audience in a solution-focused discussion centered on the over-prescribing of psychiatric medications to youth through information sharing and focused discussion questions. Objectives Critically examine the causes of over-diagnosing behavioral health problems and over-prescribing psychiatric medications in youth from the perspective of at least three stakeholders Identify one action step for each of the following sectors to reduce over-diagnosing and over-prescribing in this population: payors, administrators, clinicians, and the general public Information sharing Part I (10 min): Diagnosing psychiatric disorders in youth is a complex phenomenon that has been influenced by numerous socio-cultural factors. Experts have written about the over-pathologizing of normal child development and the role of pharmaceutical companies in this phenomenon. Youth in the US receive psychiatric medications that extend far beyond the evidence base. The use of high-risk psychiatric medications (e.g., antipsychotics) and medication regimes (e.g., three or more psychiatric medications used concurrently) among youth have increased dramatically in the past quarter century. The majority of this use is ‘off-label’, that is, it is not approved by medication regulation bodies. Further, these high-risk medications and medication regimens are more common in our most vulnerable youth – those in foster care or ‘looked after’. Many known side effects exist; long-term side effects are unknown. Fortunately, government and advocacy orgs have called for reducing the use of these high-risk medications. Discussion Part I (20 min): 1) How do we promote system-level cultural change that resists the over-pathologizing of normal child development and promotes judicious use of psychiatric medicine in youth? Consider the perspectives of various sectors: payors, health care system administrators, clinicians, caregivers, other stakeholders. 2) How do we change the experiences and requirements of medical education to promote parsimony in diagnosis and treatment of youth with behavioral health challenges? 3) How do we better inform parents, teachers, and other stakeholders about over-diagnosing and over-prescribing psychiatric medications to youth? Information sharing Part II (10 min): Our research has shown that many clinical prescribers are worried about over-diagnosing and over-prescribing and have important perspectives on why it occurs. We have also found that caregivers want to be involved in decisions about their youth’s psychiatric medications and are highly motivated to reduce high-risk medications for their youth. Once youth are on high-risk medications, one solution is to provide guidance to clinicians, youth, and families about deprescribing. Our team developed and tested a shared decision-making and deprescribing intervention in two U.S. outpatient community mental health centers serving youth with mental health needs (n=39). Nearly half (45%) of the youth who completed the treatment review phase chose to taper/deprescribe at least one medication. We observed no study-related serious adverse events or behavioral escalations. Discussion Part II (20 min): How do we make deprescribing a regular part of medicine? How do we help clinicians be more comfortable deprescribing? How do we get our intervention into the hands of clinical prescribers, clinics, and psychiatry leaders? Method We aim to engage the audience in a solution-focused discussion centered on the over-prescribing of psychiatric medications to youth through information sharing and focused discussion questions. Results The presenter will describe the background research and clinical experience related to the over-diagnosing and over-prescribing of psychiatric care to youth. The presenter will also present a novel deprescribing intervention developed and tested by her team. As a result of this seminar, the audience will engage in a critical solution-focused discussion towards reducing the harms of these phenomena. Conclusions The field of child psychiatry is finally beginning to pay attention to over-prescribing and deprescribing. This rapidly emerging topic is ripe for clinical attention and research.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,016 | 0,008 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,004 | 0,016 |
| Intégrité de la recherche | 0,023 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».